The disclosed embodiments generally pertain to one or more methods of detecting defect indications in parts, as well as to apparatus configured to implement selected aspects of the disclosed methods, and computer-readable media (transitory and non-transitory) configured to cause a computing system to perform selected aspects of the disclosed methods. More particularly, but not by way of limitation, present embodiments relate to automatic detection of defect indications in a part using volumetric computed tomography (VCT)-based data. As used herein, a “defect indication” or “indication” may refer to a portion or area of a volume that potentially may have a defect. The term “indication” will be primarily used herein.
Industrial inspection increasingly is being performed using three-dimensional (3D) volumes. A VCT scan may be performed, e.g., on a composite aircraft part under inspection, to generate a 3D stack, or “volume,” of 2D images, or “slices,” of the part. A human operator then may individually review each 2D slide to identify indication of defects typically found in composite parts, such as porosity issues and delamination.
Slice-by-slice 2D inspection of a 3D model can be time consuming, laborious and/or error prone. The operator may be required to review a large number of 2D slices of the 3D volume, alone and in relation to each other, in order to determine whether there are defects in the entire volume. For example, the operator may be required to observe subtle changes in grayscale occurring over multiple 2D images. This process is time consuming, tedious and error prone. It is also likely that the analysis will vary greatly across operators, as well as between stages of an operator's shift, e.g., due to operator fatigue.
Previous attempts to automate aspects of defect indication detection have had various problems. For instance, to reduce beam hardening and scattering artifacts, pixels or voxels of a 3D volume of a part have been “normalized” to a “standard,” e.g., an aluminum rod. However, adding a rod to the field of view may degrade the images, and this approach only works with linear computed tomography (CT) scans, not VCT. Moreover, this approach requires little or no geometric variance between the shape of the part and the shape of the standard.
In view of the aforementioned challenges and issues, it would be desirable to automate as many steps of the defect detection process as possible, so that the operator is less likely to make mistakes, will be able to review more parts per shift, and so that part inspection will be more consistent across operators and shifts.